Need Custom Training for Your Team?

Call Us

Inquire About This Course

Instructor

Dr. Stylianos Kampakis

Stylianos has worked with British Premier League club, Tottenham Hotspur FC, to build predictive models for football injuries. Currently he is working at Brandtix, building the world's first holistic football index, which measures an athlete's value using both his performance and his social media presence. In the last 5 years Stylianos has worked in machine learning and statistics. Currently he is researching how data mined from Twitter can be used to predict games in the Premier League.

Duration: 4h 24m

Course Description

Sports analytics is a new field in data science which promises to revolutionise the world of sports. The use of data to study and predict injuries has come into the front of research in the last few years and can completely change the game for team and individual sports alike.
This course deals with the use of data and advanced techniques in injury prevention and treatment. Some of the issues discussed are:
1) How can we assess the importance of a factor towards injury?
2) How can we predict injuries before they take place?
3) How can we predict recovery after an injury has taken place?
4) How should data be recorded in order to analyse the relationship with injuries?
5) How can we deliver these models in a way that can aid decision making within a club?
This course focuses on football (soccer), but the lessons taught also apply for other team (and individual sports).
The course targeted towards sports scientists, data scientists and medical practitioners. The tools used are R, Python (the most popular computer languages for data science) and Weka (a GUI tool for machine learning, useful for those who do not want to delve in coding).
The course explains all the most important concepts in statistics and machine learning and how these relate to injury prediction and exposes different use cases based on real-world examples, where data is analysed in order to aid injury-related decision making within a soccer team. The course outlines each single step in the solving the problem, from defining the problem, to the analysis, to presenting results.
Professionals from a non-technical background (sports scientists and medical practitioners) will benefit the most from the high-level explanation of the technical concepts and the tutorial on the Weka software, which does not require any coding skills.
Data scientists will benefit the most from the exposition and analysis of the use cases. The use cases replicate a real-world setting, where the data scientist has to deal with trade-offs, conflicting results and multiple stakeholders.
This course should also interest any individual or sports club that wants to learn how to use data in order to reduce injury incidence.
The course will also be of wider interest to anyone who is willing to learn how to use data science in a real world setting, as it exposes a complete overview of the basic concepts in data science, the most popular tools for data science (R, Python and Weka) and real-world scenarios, similar to those that are met in practice in many organisations.
Skills learned: Data collection, data wrangling and manipulation, statistical analysis, survival analysis, predictive modelling, machine learning
Tools used: R, Python, Weka
Soft skills: Communication skills, learn about the intricacies of sports data and working with sports clubs
Syllabus
1. Outline
a. Audience
b. Objectives
c. Tools
2. Data in sports clubs
a. Segregation, subjective opinion and lack of standards
b. Culture and organization
c. Data protocols and spurious relationships
d. Data problems
3. Aiding decision making
a. Basic principles
b. Data limitations and concept limitations
c. Aiding decision making: basic principles
d. Statistics vs Machine Learning
e. Presenting results
4. Model testing and metrics
a. Metrics overview
b. Statistical metrics
c. Machine learning model validation
5. Use case 1: Survival analysis
a. A quick introduction to R
b. Survival analysis
i. Current uses in sports
ii. Types of models
c. Dataset
i. Exploratory analysis
d. Analysis
e. Results
f. Summary and exercises
6. Use case 2: Injury prediction based on exposure records
a. Problem introduction
b. Introduction to Weka
i. Using Weka
ii. Feature selection
c. Analysis
d. Results
e. Summary and exercises
7. Use case 3: Predicting the recovery time after an injury
a. A quick introduction to Python for data analysis
b. Problem introduction
c. Analysis
d. Results
e. Interpreting results
i. Understanding the results
ii. Communicating the results to the club
f. Summary and exercises
8. Summary

What am I going to get from this course?

4) How should data be recorded in order to analyze
the relationship with injuries?

5) How to deliver these models in a way that
can aid decision making within a club?

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Skills: Data wrangling, survival analysis, predictive modelling

Tools : R, Python, Weka

Soft skills: Communication skills, learn about the intricacies of sports data and working with sports clubs

Who should take this course? Who should not?

This course should interest any individual or sports club
that wants to learn how to use data in order to reduce injury incidence. It is
meant for all levels. Beginners will mostly gain from the introduction to basic
statistical and machine learning concepts and tools. Intermediate and advanced
students will gain more from the handling of the specific use cases.

Curriculum

Module 1: Introduction to sports injury analytics

15:47

Lecture 1
Introduction

06:14

Lecture 2
Course description

07:02

Lecture 3
Exercise

02:31

Module 2: Data in sports clubs

21:42

Lecture 4
Introduction and data segregation

03:30

Lecture 5
Subjective opinion and data standards

05:17

Lecture 6
Culture and data collection protocols

05:10

Lecture 7
Data problems

07:45

Module 3: Aiding decision making

29:54

Lecture 8
Introduction and data limitations

02:48

Lecture 9
Epistemological limitations

06:33

Lecture 10
Sports related limitations

01:51

Lecture 11
Principles of decision making

04:12

Lecture 12
Statistics vs Machine Learning

08:08

Lecture 13
Metrics and presenting results

06:22

Module 4: Model testing and metrics

31:54

Lecture 14
Introduction and statistical models

06:02

Lecture 15
Lecture

06:34

Lecture 16
Machine learning tips

03:45

Lecture 17
Metrics classification

08:19

Lecture 18
Metrics for regression

07:14

Module 5: Survival analysis

47:00

Lecture 19
Survival analysis introduction

03:01

Lecture 20
Types of survival models

03:57

Lecture 21
Survival analysis

02:16

Lecture 22
Problem statement

01:38

Lecture 23
Data exploration

08:14

Lecture 24
R primer

03:17

Lecture 25
Cox regression

07:07

Lecture 26
Parametric models

09:31

Lecture 27
Survival curves

06:23

Lecture 28
Summary

01:36

Module 6: Injury prediction based on exposure records

01:01:34

Lecture 29
Problem introduction

04:21

Lecture 30
Solution framework

03:02

Lecture 31
Applying the model

07:29

Lecture 32
Problem specification

02:57

Lecture 33
Weka introduction

04:32

Lecture 34
Data preprocessing

05:19

Lecture 35
Weka experimenter

09:30

Lecture 36
Classification

10:36

Lecture 37
Feature selection

10:46

Lecture 38
Summary

03:02

Module 7: Predicting the recovery time after an injury

49:27

Lecture 39
Introduction

01:43

Lecture 40
Python primer

11:00

Lecture 41
Pandas introduction

05:14

Lecture 42
Data cleaning

09:43

Lecture 43
Data and predictive modelling

05:20

Lecture 44
Predictive modelling - part A

04:37

Lecture 45
Predictive modelling - Part B

05:21

Lecture 46
Results

03:55

Lecture 47
Exercises

02:34

Module 8: Course Summary

06:28

Lecture 48
Course summary and conclusions

06:28

Reviews

8 Reviews

Jodi D

December, 2016

Thomas S

May, 2017

The course has a practical value for sports managers and beneficial for them. They can apply the modern technology of data science to manage their individual sports field. Indeed, it is possible to reduce injury incidents with predictive models and data application. I find the course is of immense help and beneficial emotionally as sports has an emotional appeal. The learning experience of the course is great. The quality of the course is also excellent and all encompassing. Predicting injuries and recoveries really greatly help in managing a sport.

Fato C

May, 2017

An excellent course. Learning data science by working on real-world problems on sports injury prediction has great appeal to sports fraternity. The content is very much education focused.

Kevin L

May, 2017

The learning experience of this course is very active. It definitely carries a beneficial value to those who can learn from this course along with their demanding physical experience in the sports.

Rashana P

July, 2017

Great course! This course is not just teaching you how to use data science. You study how to link your data with your sports problem and support people to arrive at a better resolution.

Jonathan W

July, 2017

Great introduction and lessons regarding data science for sports. Well planned and effective courses! I learn a lot about data techniques and the whole process of data analysis, including preparing the data analysis process with scientific guidance, making persuasive illustrations and giving a high quality presentation!! I have learned a lot!

Prashanth K

July, 2017

Excellent Course!!! Enjoy the learning experience!!! Highly recommend this course to anyone who wants to learn data science for sports. Good Luck.

Rabia Nur D

July, 2017

Good subject for a newcomer to become familiarized with data science. Very extensive and precise content presentation. Usable and useful learning tools

Inquire About This Course

Please fill in the details and our support team will get back to you within 1 business day.